Índice
- 1 Imports
- 2 Excel
- 3 Variação intra-anual. Totais
- 4 Variação inter-anual. Totais
- 5 Variação intermensal. Totais
- 6 Variação intra_anual em cada bioma
- 7 Variação interanual em cada bioma
- 8 Variação intra-anual em cada clima
- 9 Variação inter-anual em cada clima
- 10 Variação inter-anual por mês em cada clima
- 11 Bioma 0 N/S Rock & Ice
- 12 Bioma 1 Tropical & Subtropical Moist Broadleaf Forests
- 13 Bioma 2 Tropical & Subtropical Dry Broadleaf Forests
- 14 Bioma 3 Tropical & Subtropical Coniferous Forests
- 15 Bioma 4 N/S Temperate Broadleaf & Mixed Forests
- 16 Biome 5 Temperate Conifer Forests
- 17 Biome 6 Boreal Forests/Taiga
- 18 Bioma 7 (mod) Tropical & Subtropical Grasslands, Savannas & Shrublands
- 19 Bioma 8 (mod) Temperate Grasslands, Savannas & Shrublands
- 20 Bioma 9 (mod) Flooded Grasslands & Savannas
- 21 Biome 10 (mod) Montane Grasslands & Shrublands
Nova shape¶
Imports¶
In [1]:
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
import numpy as np
import matplotlib.cm as cm
import matplotlib.colors as cls
from pandas import Series, DataFrame
from mpl_toolkits.axes_grid1.inset_locator import inset_axes, zoomed_inset_axes, mark_inset
import matplotlib.ticker as ticker
from collections import defaultdict, namedtuple
from pandas import ExcelWriter
import xlsxwriter
import pandas as pd
import calendar
import pickle
import datetime
from scipy import stats
import itertools
import statistics
# set seaborn plot style
import seaborn as sns
sns.set()
sns.set_context("paper")
sns.set_style("whitegrid")
# set default font
plt.rcParams["font.family"] = "Times New Roman"
In [7]:
def get_ecos():
m = Basemap(projection='cyl')
shpf = m.readshapefile('C:/Users/alpha/Documents/MODIS/MCD64A1/newreprojected/newreprojected','ecos',linewidth=0.1)
return m.ecos_info, m.ecos
In [6]:
m = Basemap(projection='cyl')
shpf = m.readshapefile('C:/Users/alpha/Documents/MODIS/MCD64A1/newreprojected/newreprojected','ecos',linewidth=0.1)
ecos_info, ecos = m.ecos_info, m.ecos
In [4]:
%qtconsole
In [10]:
ecos_info[0].keys()
Out[10]:
In [11]:
months = [calendar.month_name[i +1][:3] for i in range(12)]
In [12]:
years = [str(y) for y in range(2001,2018)]
In [42]:
ecoids = [e['ECO_ID'] for e in ecos_info]
bionums = [e['BIOME_NUM'] for e in ecos_info]
bionames = [e['BIOME_NAME'] for e in ecos_info]
ids = [e['ID'] for e in ecos_info]
climes = [e['GRIDCODE'] for e in ecos_info]
continents = [e['CONTINENT'] for e in ecos_info]
countries = [e['FIPS_CNTRY'] for e in ecos_info]
areas= [e['Area_polig'] for e in ecos_info]
In [796]:
cntnames= [e['CNTRY_NAME'] for e in ecos_info]
In [59]:
for i ,id in enumerate(ecoids):
if id == 0:
bionums[i] = 0.0
In [20]:
result = {}
for i in range(1,18):
suf = '20{0:02d}'.format(i)
in_file= 'newresult' + suf
result[suf] = DataFrame(np.load(in_file), columns =months)
In [60]:
for i in range(1,18):
suf = '20{0:02d}'.format(i)
result[suf] = result[suf].assign(eco_id=ecoids,biome_num =bionums,biome_name=bionames,idd=ids,clime=climes,continent=continents,country=countries, area = areas)
In [92]:
grouped = result['2001'][ ['eco_id','continent','clime','country','idd' ,'area'] ].groupby(['eco_id','continent','clime','country','idd' ,'area'])
areas_row = []
for a, _ in grouped:
areas_row.append(a[-1])
In [105]:
attrs_table = {}
for yr, df in result.items():
attrs_table[yr] = df[ months + ['eco_id','continent','clime','country','idd','biome_num' ,'area']].groupby(['eco_id','continent','clime','country','idd','biome_num','area']).sum()
In [110]:
at_table = pd.concat(attrs_table)
Excel¶
In [111]:
writer = ExcelWriter('newshape.xlsx',engine='xlsxwriter')
In [112]:
at_table.to_excel(writer,sheet_name='Burned Pixels',engine='xlsxwriter')
In [113]:
writer.save()
In [786]:
new_at_table = at_table.reset_index()
In [788]:
writer = ExcelWriter('simplyrows_newshape.xlsx',engine='xlsxwriter')
new_at_table.to_excel(writer,sheet_name='Burned Pixels',engine='xlsxwriter')
writer.save()
In [206]:
plt.rcParams['mathtext.fontset'] = 'custom'
plt.rcParams['mathtext.rm'] = "Times New Roman"
plt.rcParams['mathtext.it'] = "Times New Roman"
plt.rcParams['mathtext.bf'] = "Times New Roman"
In [207]:
g = sns.regplot(np.arange(12),attrs_table['2002'].sum()/1e6, fit_reg= False)
ylabels = g.get_yticklabels()
g.set_xticklabels(months[::2], fontsize=15)
g.set_xticks(np.arange(0,12,2))
g.set_ylabel('Burned Pixels $(10^6)$', fontsize=15)
plt.setp(ylabels,fontsize=15)
#g.set(xticklabels=months[::2],xticks=np.arange(0,12,2), ylabel='Burned Pixels $(10^6)$', ylabelsize=15)
plt.show()
In [210]:
plt.plot(np.arange(12),attrs_table['2002'].sum()/1e6)
g =plt.gca()
ylabels = g.get_yticklabels()
g.set_xticklabels(months[::2], fontsize=15)
g.set_xticks(np.arange(0,12,2))
g.set_ylabel('Burned Pixels $(10^6)$', fontsize=15)
plt.setp(ylabels,fontsize=15)
#g.set(xticklabels=months[::2],xticks=np.arange(0,12,2), ylabel='Burned Pixels $(10^6)$', ylabelsize=15)
plt.show()
In [211]:
plt.scatter(np.arange(12),attrs_table['2002'].sum()/1e6)
g =plt.gca()
ylabels = g.get_yticklabels()
g.set_xticklabels(months[::2], fontsize=15)
g.set_xticks(np.arange(0,12,2))
g.set_ylabel('Burned Pixels $(10^6)$', fontsize=15)
plt.setp(ylabels,fontsize=15)
#g.set(xticklabels=months[::2],xticks=np.arange(0,12,2), ylabel='Burned Pixels $(10^6)$', ylabelsize=15)
plt.show()
In [215]:
at_table.shape
Out[215]:
In [221]:
at_table.loc['2001',0,'Antarctica']
Out[221]:
In [222]:
at_table.sum()
Out[222]:
Variação intra-anual. Totais¶
In [246]:
fig, ax = plt.subplots()
ax.plot( np.arange(12),at_table.sum()/1e6)
ax.set_xticks(np.arange(12))
ax.set_xticklabels(months, fontsize=15)
ax.set_title('Intra-annual Variability',fontsize=15)
labels_x = ax.get_xticklabels()
labels_y = ax.get_yticklabels()
off =ax.get_yaxis().get_offset_text()
off.set_x(-.07)
off.set_fontsize(12)
plt.setp(labels_y,fontsize=15)
plt.ylabel(r"Burned Pixels $(10^6)$", fontsize = 15)
plt.tight_layout()
plt.show()
Variação inter-anual. Totais¶
In [239]:
at_table.index.names = ['year', 'eco_id', 'continent', 'clime', 'country', 'idd', 'biome_num', 'area']
In [244]:
at_table.groupby('year').sum().sum(axis=1)
Out[244]:
In [245]:
fig, ax = plt.subplots()
labels = years[::2]
ax.plot(years, at_table.groupby('year').sum().sum(axis=1)/1e6)
ax.set_xticks(labels)
ax.set_xticklabels(labels, fontsize=15)
ax.set_title('Interannual Variability',fontsize=15)
labels_x = ax.get_xticklabels()
labels_y = ax.get_yticklabels()
off =ax.get_yaxis().get_offset_text()
off.set_x(-.07)
off.set_fontsize(12)
plt.setp(labels_y,fontsize=15)
plt.ylabel(r"Burned Pixels ($10^6$)", fontsize = 15)
plt.tight_layout()
plt.show()
Variação intermensal. Totais¶
In [249]:
at_table.groupby('year').sum().values.flatten()
Out[249]:
In [251]:
ym = itertools.product(years,months)
In [262]:
ymlabels = [ m + ' ' + y[-2:] for y, m in ym]
fig, ax = plt.subplots(figsize=(16,12))
labels = ymlabels[::6]
ax.plot( np.arange(12*17),at_table.groupby('year').sum().values.flatten()/1e6)
ax.axhline(y=statistics.mean(at_table.groupby('year').sum().values.flatten()/1e6), color='r', linestyle='--', label='mean')
ax.set_xticks(np.arange(0,12*17,6))
ax.set_xticklabels(labels,fontsize=12, rotation=30)
labels_y = ax.get_yticklabels()
plt.setp(labels_y,fontsize=15)
plt.ylabel(r"Burned Pixels ($10^6$)", fontsize = 15)
ax.set_title('Intra-monthly Variability',fontsize=15)
plt.legend(fontsize=15)
#prop={'size': 6}
plt.show()
Variação intra_anual em cada bioma¶
In [264]:
at_table.groupby('biome_num').sum()
Out[264]:
In [283]:
at_table.loc['2001',0, 'Antarctica',61,'AY',2250 ]
Out[283]:
In [291]:
areas = at_table.loc['2001'].index.get_level_values(6)
In [296]:
at_table.index.get_level_values(7)
Out[296]:
In [297]:
141338/17
Out[297]:
In [303]:
all_areas =at_table.index.get_level_values(7)
In [312]:
at_table_cp = at_table.copy()
In [304]:
#at_table_cp.areas = all_areas
In [313]:
at_table_cp.reset_index(inplace=True)
In [325]:
agg_dict = {month : 'sum' for month in months}
In [326]:
div_17 = lambda x: sum(x)/17
In [330]:
agg_dict['area'] = div_17
In [333]:
biomes_table = at_table_cp.groupby('biome_num').agg(agg_dict)
In [341]:
biomes_table[months].div(biomes_table['area'], axis =0)
Out[341]:
In [338]:
biomes_table['area']
Out[338]:
In [345]:
# for serialization:
tabelas = {'multindex': at_table, 'simpleindex':at_table_cp}
In [346]:
with open('tabelas','wb') as f:
f.write(pickle.dumps(tabelas))
In [369]:
biomes_intra = biomes_table[months].div(biomes_table['area'], axis =0)
In [356]:
biome_names_dict = dict( (num, name) for num, name in zip(bionums, bionames))
Mesma escala¶
In [372]:
fig, ax = plt.subplots(5,3, figsize =(18,12) , sharey=True)
labels = months
ax = ax.ravel()
for i in range(15):
#slope, intercept, r_value, p_value, std_err = stats.linregress(np.arange(12), monbio[i*3 + j])
#ax[i][j].plot(months, intercept + slope*years_float, 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
ax[i].plot(months, biomes_intra.iloc[i], 'o')
ax[i].set_xticks(np.arange(12))
ax[i].set_xticklabels(labels,fontsize=15)
labels_y = ax[i].get_yticklabels()
plt.setp(labels_y,fontsize=15)
ax[i].set_title(biome_names_dict[i],fontsize=15)
#ax[i].legend(fontsize=15)
fig.tight_layout()
plt.show()
Sem escala comum¶
In [373]:
fig, ax = plt.subplots(5,3, figsize =(18,12))
labels = months
ax = ax.ravel()
for i in range(15):
#slope, intercept, r_value, p_value, std_err = stats.linregress(np.arange(12), monbio[i*3 + j])
#ax[i][j].plot(months, intercept + slope*years_float, 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
ax[i].plot(months, biomes_intra.iloc[i], 'o')
ax[i].set_xticks(np.arange(12))
ax[i].set_xticklabels(labels,fontsize=15)
labels_y = ax[i].get_yticklabels()
plt.setp(labels_y,fontsize=15)
ax[i].set_title(biome_names_dict[i],fontsize=15)
#ax[i].legend(fontsize=15)
fig.tight_layout()
plt.show()
Variação interanual em cada bioma¶
In [393]:
at_table_cp[at_table_cp.year == '2001'].groupby('biome_num').sum().div(biomes_table['area'], axis=0)[months]
Out[393]:
In [390]:
biomes_table['area'].size
Out[390]:
In [397]:
## test 2001 may bionum = 5 assert 3.547547e-04
bp = at_table_cp[at_table_cp.year == '2001'].groupby('biome_num').sum().iloc[5]['May']
area_5 = biomes_table['area'][5]
assert bp/area_5 == 3.547547e-04, print((bp/area_5 - 3.547547e-04 )*100/3.547547e-04)
In [399]:
at_table_cp[at_table_cp.year == '2001'].groupby('biome_num').sum().div(biomes_table['area'], axis=0)[months].sum(axis=1)
Out[399]:
In [400]:
biomes_inter = [at_table_cp[at_table_cp.year == yr].groupby('biome_num').sum().div(biomes_table['area'], axis=0)[months].sum(axis=1) for yr in years]
In [405]:
biomes_inter = DataFrame(biomes_inter).T
In [406]:
biomes_inter
Out[406]:
Mesma escala¶
In [414]:
fig, ax = plt.subplots(5,3, figsize =(18,12) , sharey=True)
labels = years[::2]
ax = ax.ravel()
for i in range(15):
slope, intercept, r_value, p_value, std_err = stats.linregress(range(17), biomes_inter.iloc[i])
ax[i].plot(years, intercept + slope*range(17), 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
ax[i].plot(years, biomes_inter.iloc[i], 'o', label='')
ax[i].set_xticks(labels)
ax[i].set_xticklabels(labels,fontsize=15)
labels_y = ax[i].get_yticklabels()
plt.setp(labels_y,fontsize=15)
ax[i].set_title(biome_names_dict[i],fontsize=15)
ax[i].legend(fontsize=15)
fig.tight_layout()
plt.show()
Sem escala comum¶
In [416]:
fig, ax = plt.subplots(5,3, figsize =(18,12) )
labels = years[::2]
ax = ax.ravel()
for i in range(15):
#slope, intercept, r_value, p_value, std_err = stats.linregress(range(17), biomes_inter.iloc[i])
#ax[i].plot(years, intercept + slope*range(17), 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
ax[i].plot(years, biomes_inter.iloc[i], label='')
ax[i].set_xticks(labels)
ax[i].set_xticklabels(labels,fontsize=15)
labels_y = ax[i].get_yticklabels()
plt.setp(labels_y,fontsize=15)
ax[i].set_title(biome_names_dict[i],fontsize=15)
ax[i].legend(fontsize=15)
fig.tight_layout()
plt.show()
In [417]:
biomes_inter
Out[417]:
In [419]:
at_table_cp[at_table_cp.year == '2001'].groupby('biome_num').sum().div(biomes_table['area'], axis=0)[months].sum()
Out[419]:
Variação intra-anual em cada clima¶
In [422]:
no_climas = len(set(climes));no_climas
Out[422]:
In [427]:
climes_table = at_table_cp.groupby('clime').agg(agg_dict)
In [428]:
climes_table[months].div(climes_table['area'], axis =0)
Out[428]:
In [430]:
climes_intra = climes_table[months].div(climes_table['area'], axis =0)
Mesma escala¶
In [431]:
climanames = [e['Clima_Name'] for e in ecos_info]
In [434]:
climes_intra.index[0]
Out[434]:
In [432]:
clima_names_dict = dict( (num, name) for num, name in zip(climes, climanames))
In [466]:
# 11 x 3, 31 climas
fig, ax = plt.subplots(nrows=11,ncols=3, figsize =(18,40) , sharey=True)
labels = months
ax = ax.ravel()
for i in range(31):
#slope, intercept, r_value, p_value, std_err = stats.linregress(np.arange(12), monbio[i*3 + j])
#ax[i][j].plot(months, intercept + slope*years_float, 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
ax[i].plot(months, climes_intra.iloc[i], 'o')
ax[i].set_xticks(np.arange(12))
ax[i].set_xticklabels(labels,fontsize=15)
labels_y = ax[i].get_yticklabels()
plt.setp(labels_y,fontsize=15)
ax[i].set_title(clima_names_dict[climes_intra.index[i]],fontsize=15)
#ax[i].legend(fontsize=15)
for i in range(31,33):
ax[i].set_visible(False)
fig.tight_layout()
plt.show()
Sem escala comum¶
In [467]:
fig, ax = plt.subplots(nrows=11,ncols=3, figsize =(18,40) )
labels = months
ax = ax.ravel()
for i in range(31):
#slope, intercept, r_value, p_value, std_err = stats.linregress(np.arange(12), monbio[i*3 + j])
#ax[i][j].plot(months, intercept + slope*years_float, 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
ax[i].plot(months, climes_intra.iloc[i], 'o')
ax[i].set_xticks(np.arange(12))
ax[i].set_xticklabels(labels,fontsize=15)
labels_y = ax[i].get_yticklabels()
plt.setp(labels_y,fontsize=15)
ax[i].set_title(clima_names_dict[climes_intra.index[i]],fontsize=15)
#ax[i].legend(fontsize=15)
for i in range(31,33):
ax[i].set_visible(False)
fig.tight_layout()
plt.show()
In [471]:
clima_names_dict.values()
Out[471]:
Variação inter-anual em cada clima¶
In [488]:
at_table_cp[at_table_cp['year']=='2001'].groupby('clime').sum().div(climes_table['area'], axis=0)[months].sum(axis=1)
Out[488]:
In [493]:
climas_inter = [at_table_cp[at_table_cp['year']==yr].groupby('clime').sum().div(climes_table['area'], axis=0)[months].sum(axis=1) for yr in years]
In [495]:
climas_inter = DataFrame(climas_inter).T
In [496]:
climas_inter
Out[496]:
Mesma escala¶
In [500]:
fig, ax = plt.subplots(nrows=11,ncols=3, figsize =(18,40) , sharey=True)
labels = years[::2]
ax = ax.ravel()
for i in range(31):
slope, intercept, r_value, p_value, std_err = stats.linregress(range(17), climas_inter.iloc[i])
ax[i].plot(years, intercept + slope*range(17), 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
ax[i].plot(years, climas_inter.iloc[i], 'o', label='')
ax[i].set_xticks(labels)
ax[i].set_xticklabels(labels,fontsize=15)
labels_y = ax[i].get_yticklabels()
plt.setp(labels_y,fontsize=15)
ax[i].set_title(clima_names_dict[climas_inter.index[i]],fontsize=15)
ax[i].legend(fontsize=15)
for i in range(31,33):
ax[i].set_visible(False)
fig.tight_layout()
plt.show()
Escala comum¶
In [501]:
fig, ax = plt.subplots(nrows=11,ncols=3, figsize =(18,40) )
labels = years[::2]
ax = ax.ravel()
for i in range(31):
slope, intercept, r_value, p_value, std_err = stats.linregress(range(17), climas_inter.iloc[i])
ax[i].plot(years, intercept + slope*range(17), 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
ax[i].plot(years, climas_inter.iloc[i], 'o', label='')
ax[i].set_xticks(labels)
ax[i].set_xticklabels(labels,fontsize=15)
labels_y = ax[i].get_yticklabels()
plt.setp(labels_y,fontsize=15)
ax[i].set_title(clima_names_dict[climas_inter.index[i]],fontsize=15)
ax[i].legend(fontsize=15)
for i in range(31,33):
ax[i].set_visible(False)
fig.tight_layout()
plt.show()
Variação inter-anual por mês em cada clima¶
In [530]:
groupedby_year_clima = at_table_cp.groupby(['clime','year']).sum()
In [531]:
groupedby_year_clima = groupedby_year_clima[months].div(groupedby_year_clima.area, axis =0)
In [537]:
groupedby_year_clima =groupedby_year_clima.reset_index()
In [544]:
groupedby_year_clima[groupedby_year_clima['clime'] == 11][months].T.iloc[0]
Out[544]:
In [567]:
labels = years[::2]
for climcode in clima_names_dict.keys():
df = groupedby_year_clima[groupedby_year_clima['clime'] == climcode][months].T
fig, ax = plt.subplots(4,3, figsize =(20,12) , sharey= True)
ax = ax.ravel()
fig.suptitle(clima_names_dict[climcode],fontsize=15)
for i in range(12):
data = df.iloc[i]
slope, intercept, r_value, p_value, std_err = stats.linregress(range(17), data)
ax[i].plot(years, intercept + slope*range(17), 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
ax[i].plot(years, data, 'o', label='_nolegend_')
ax[i].set_xticks(labels)
ax[i].set_xticklabels(labels,fontsize=15)
labels_y = ax[i].get_yticklabels()
plt.setp(labels_y,fontsize=15)
ax[i].set_title(calendar.month_name[i +1],fontsize=15)
ax[i].legend(fontsize=15)
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()
In [569]:
labels = years[::2]
for climcode in clima_names_dict.keys():
df = groupedby_year_clima[groupedby_year_clima['clime'] == climcode][months].T
fig, ax = plt.subplots(4,3, figsize =(20,12) )
ax = ax.ravel()
fig.suptitle(clima_names_dict[climcode],fontsize=15)
for i in range(12):
data = df.iloc[i]
#slope, intercept, r_value, p_value, std_err = stats.linregress(range(17), data)
#ax[i].plot(years, intercept + slope*range(17), 'r', label=r"$R^2$: {:5.3f} ".format(r_value**2))
ax[i].plot(years, data, 'o', label='_nolegend_')
ax[i].set_xticks(labels)
ax[i].set_xticklabels(labels,fontsize=15)
labels_y = ax[i].get_yticklabels()
plt.setp(labels_y,fontsize=15)
ax[i].set_title(calendar.month_name[i +1],fontsize=15)
ax[i].legend(fontsize=15)
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()
In [592]:
# as linhas seguintes dão o mesmo resultado
at_table_cp.groupby(['year','clime']).sum()[months];
pd.pivot_table(at_table_cp, values=months, index=['year', 'clime'],aggfunc=np.sum)[months];
In [700]:
notincluded13 = pickle.loads(open('notincluded2013','rb').read())
df_notinc13 = DataFrame(notincluded13)
df_notinc13.to_csv('not_included13.csv', header=False)
In [610]:
notincluded17 = pickle.loads(open('notincluded2017','rb').read())
df_notinc17 = DataFrame(notincluded17)
df_notinc17.to_csv('not_included17.csv', header=False)
In [635]:
plt.close('all')
In [646]:
eco_bio = dict (zip(ecoids,bionums))
In [645]:
eco_bio[14]
Out[645]:
In [665]:
bio_eco = defaultdict(list)
In [666]:
unique_ecoids =list(set(ecoids))
for ecoid in unique_ecoids:
bio_eco[eco_bio[ecoid]].append(ecoid)
In [669]:
bio_eco[0]
Out[669]:
In [668]:
biome_names_dict[0]
Out[668]:
In [670]:
len(ecos)
Out[670]:
In [757]:
'''from itertools import chain
concatenated = chain(range(1,11), range(12, 15))
ecos_in_bio = dict()
for bionum in concatenated:
ecos_in_bio[bionum] = [ ecos_info.index(eco_info) for eco_info in ecos_info if eco_info['BIOME_NUM'] == bionum]
ecos_in_bio[0] = [ ecos_info.index(eco_info) for eco_info in ecos_info if eco_info['BIOME_NAME'] == 'N/A']
ecos_in_bio[11] = [ ecos_info.index(eco_info) for eco_info in ecos_info if eco_info['BIOME_NAME'] == 'Tundra']'''
Out[757]:
In [760]:
ecos_in_bio = defaultdict(list)
In [761]:
for eco_info in ecos_info:
if eco_info['BIOME_NAME'] == 'N/A':
ecos_in_bio[0].append(ecos_info.index(eco_info))
elif eco_info['BIOME_NAME'] == 'Tundra':
ecos_in_bio[11].append(ecos_info.index(eco_info))
else:
ecos_in_bio[eco_info['BIOME_NUM']].append(ecos_info.index(eco_info))
In [699]:
notincluded14 = pickle.loads(open('notincluded2014','rb').read())
df_notinc14 = DataFrame(notincluded14)
df_notinc14.to_csv('not_included14.csv', header=False)
In [701]:
df_eco0 = DataFrame(ecos[0])
df_eco0.to_csv('eco0.csv', header=False)
In [722]:
plt.set_cmap('OrRd')
In [730]:
plt.cm.OrRd(255)
Out[730]:
In [732]:
plt.cm.OrRd?
In [733]:
count = 0
for bionum in bionums:
if bionum == 0:
count += 1
print(count)
In [735]:
%time ecas_info, ecas = get_ecos()
In [742]:
count = 0
for e in ecas_info:
if e['BIOME_NAME'] == 'N/A' :
count += 1
print(count)
In [754]:
plt.close('all')
In [755]:
len(ecos_in_bio[0])
Out[755]:
Bioma 0 N/S Rock & Ice¶
Se o continente for south america ou antarctica é south, e north nos outros casos. Rock and Ice
In [773]:
biome_names_dict[0]
Out[773]:
In [766]:
south0 = ['South America', 'Antarctica']
In [769]:
biome0S = [ix for ix in ecos_in_bio[0] if ecos_info[ix]['CONTINENT'] in south0]
In [765]:
list(set(continents))
Out[765]:
In [767]:
biome0N = [ix for ix in ecos_in_bio[0] if ecos_info[ix]['CONTINENT'] not in south0]
In [770]:
len(biome0S) + len(biome0N)
Out[770]:
Bioma 1 Tropical & Subtropical Moist Broadleaf Forests¶
In [772]:
biome_names_dict[1]
Out[772]:
Bioma 2 Tropical & Subtropical Dry Broadleaf Forests¶
In [775]:
biome_names_dict[2]
Out[775]:
Bioma 3 Tropical & Subtropical Coniferous Forests¶
In [776]:
biome_names_dict[3]
Out[776]:
Bioma 4 N/S Temperate Broadleaf & Mixed Forests¶
In [777]:
biome_names_dict[4]
Out[777]:
In [778]:
bionums_ar = np.array(bionums)
continents_ar = np.array(continents)
In [779]:
continentsbio4 = continents_ar[bionums_ar==4]
In [782]:
np.unique(continentsbio4)
Out[782]:
In [783]:
south4= ['Australia', 'Oceania', 'South America']
In [805]:
np.unique(countries_ar[np.logical_and(bionums_ar==4, continents_ar=='Asia')])
Out[805]:
Biome 5 Temperate Conifer Forests¶
In [784]:
biome_names_dict[5]
Out[784]:
In [806]:
np.unique(continents_ar[bionums_ar==5])
Out[806]:
In [807]:
np.unique(countries_ar[np.logical_and(bionums_ar==5, continents_ar=='Asia')])
Out[807]:
Biome 6 Boreal Forests/Taiga¶
In [789]:
biome_names_dict[6]
Out[789]:
Bioma 7 (mod) Tropical & Subtropical Grasslands, Savannas & Shrublands¶
In [791]:
biome_names_dict[7]
Out[791]:
In [792]:
continentsbio7 = continents_ar[bionums_ar==7]
In [793]:
np.unique(continentsbio7)
Out[793]:
Asia¶
In [797]:
countries_ar = np.array(cntnames)
In [798]:
np.unique(countries_ar[np.logical_and(continents_ar == 'Asia', bionums_ar == 7)])
Out[798]:
In [799]:
np.unique(countries_ar[np.logical_and(continents_ar == 'Africa', bionums_ar == 7)])
Out[799]:
In [800]:
np.unique(countries_ar[np.logical_and(continents_ar == 'South America', bionums_ar == 7)])
Out[800]:
Bioma 8 (mod) Temperate Grasslands, Savannas & Shrublands¶
In [801]:
biome_names_dict[8]
Out[801]:
In [802]:
np.unique(continents_ar[bionums_ar==8])
Out[802]:
In [804]:
np.unique(countries_ar[np.logical_and(bionums_ar==8, continents_ar=='Asia')])
Out[804]:
In [808]:
south8 = [ 'Australia', 'Oceania', 'South America']
Bioma 9 (mod) Flooded Grasslands & Savannas¶
In [810]:
biome_names_dict[9]
Out[810]:
In [811]:
np.unique(continents_ar[bionums_ar==9])
Out[811]:
In [812]:
np.unique(countries_ar[np.logical_and(bionums_ar==9, continents_ar=='Asia')])
Out[812]:
In [813]:
np.unique(countries_ar[np.logical_and(bionums_ar==9, continents_ar=='South America')])
Out[813]:
In [814]:
np.unique(countries_ar[np.logical_and(bionums_ar==9, continents_ar=='Africa')])
Out[814]:
Biome 10 (mod) Montane Grasslands & Shrublands¶
In [815]:
biome_names_dict[10]
Out[815]:
In [816]:
np.unique(continents_ar[bionums_ar==10])
Out[816]:
In [817]:
np.unique(countries_ar[np.logical_and(bionums_ar==10, continents_ar=='Asia')])
Out[817]:
QGIS: "BIOME_NUM" = 10 and "CONTINENT" = 'Asia' and "CNTRY_NAME" IN ('Indonesia','Malaysia','Papua New Guinea')
A Malásia fica na verdade a 6° norte.
In [818]:
np.unique(countries_ar[np.logical_and(bionums_ar==10, continents_ar=='Africa')])
Out[818]:
In [822]:
bioma10AfricaNorte = pd.read_csv('bioma10AfricaNorte.csv', sep=';')
In [878]:
bioma10AfricaNorte;
In [826]:
bioma10AfricaNorte['CNTRY_NAME'].unique()
Out[826]:
In [827]:
bioma10AfricaNorte['CNTRY_NAME'].size
Out[827]:
In [840]:
cntbio10AfrNor = bioma10AfricaNorte['FIPS_CNTRY'].unique()
In [879]:
at_table_cp[np.logical_and(np.isin(at_table_cp.country,cntbio10AfrNor ),at_table_cp.biome_num == 10)].size
Out[879]: